Module 13: Model Deployment
Learn to deploy machine learning models to production.
What You'll Learn
- Model Serialization
- REST APIs with Flask/FastAPI
- Docker for ML
- Cloud Deployment
- Model Monitoring
- Best Practices for Production
Topics Covered
1. Model Serialization
- Pickle: Python's native serialization
- Joblib: Better for NumPy arrays
- H5/HDF5: For Keras models
- ONNX: Cross-platform format
- Saving: Architecture + weights
2. REST APIs
- Flask: Simple web framework
- Creating endpoints
- Request/response handling
- Error handling
- FastAPI: Modern, fast framework
- Automatic documentation
- Type hints
- Async support
- API Design: Best practices
3. Docker for ML
- Containerization: Package model + dependencies
- Dockerfile: Define container
- Docker Images: Build and run
- Docker Compose: Multi-container apps
- Benefits: Reproducibility, portability
4. Cloud Deployment
- AWS: SageMaker, EC2, Lambda
- Google Cloud: Vertex AI, Cloud Run
- Azure: Azure ML, Container Instances
- Heroku: Simple deployment
- Choosing Platform: Based on needs
5. Production Server Setup
- NGINX Configuration: Reverse proxy, load balancing, SSL termination
- SSL/TLS Setup: Let's Encrypt certificates, auto-renewal
- Domain Configuration: DNS setup, subdomain routing
- Security: Rate limiting, API authentication, input validation
- Error Handling: Structured error responses, logging
- AWS EC2 Setup: Instance configuration, systemd services, firewall
6. Model Serving
- Batch Inference: Process in batches
- Real-time Inference: Low latency
- A/B Testing: Compare model versions (statistical significance, multi-armed bandits, sequential testing)
- Canary Deployments: Gradual rollout
7. Model Monitoring
- Performance Metrics: Track accuracy over time
- Data Drift: Detect distribution changes
- Model Drift: Performance degradation
- Logging: Track predictions and errors
- Alerts: Notify on issues
Learning Objectives
By the end of this module, you should be able to:
- Serialize and load models
- Create REST APIs for models
- Containerize ML applications
- Deploy to cloud platforms
- Configure production servers (NGINX, SSL, domain)
- Implement security best practices (rate limiting, authentication)
- Monitor deployed models
Projects
- Flask API: Deploy a model with Flask
- FastAPI Service: Build FastAPI service
- Docker Container: Containerize ML app
- Cloud Deployment: Deploy to AWS/GCP/Azure
- Monitoring Dashboard: Track model performance
Key Concepts
- API Endpoints: Expose model as service
- Containerization: Package everything together
- Scalability: Handle multiple requests
- Monitoring: Track model health
- Versioning: Manage model versions
Documentation & Learning Resources
FastAPI:
Docker:
Flask:
MLflow:
Free Courses:
- FastAPI Course (YouTube): Free tutorial
- Docker Course (YouTube): Free comprehensive course
- ML Deployment (Coursera): Free audit available
Tutorials:
- Deploying ML Models (Real Python)
- Docker for Data Scientists
- ML Model Deployment Guide
- Setting up NGINX as Reverse Proxy (DigitalOcean)
- SSL Certificate Setup with Let's Encrypt (DigitalOcean)
- Production FastAPI Deployment (TestDriven.io)
NGINX:
SSL/TLS & Security:
- Let's Encrypt Documentation
- Certbot User Guide
- SSL/TLS Best Practices (Mozilla)
- OWASP API Security Top 10
Domain & DNS:
Cloud Platforms:
- AWS SageMaker Documentation
- AWS EC2 Documentation
- Google Cloud AI Platform
- Azure ML Documentation
- Heroku Deployment Guide
Video Tutorials:
- FastAPI Tutorial (Corey Schafer)
- Docker Tutorial (TechWorld with Nana)
- NGINX Tutorial (LearnLinuxTV)
- SSL/TLS Explained (PowerCert)
- ML Deployment (Sentdex)
Additional Resources:
- Advanced Topics →: Advanced deployment patterns, Kubernetes, edge deployment
- Project Tutorial →: Step-by-step model deployment project
- Quick Reference →: Quick lookup guide for model deployment
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